Spatiotemporal Effects and Driving Factors of Water Pollutants Discharge in Beijing–Tianjin–Hebei Region
The problem of water pollution is a social issue in China requiring immediate and urgent solutions. In the Beijing–Tianjin–Hebei region, the contradiction between preserving the ecological environment and facilitating sustainable economic development is particularly acute. This study analyzed the sp...
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doaj-d4841e69e0bc4735a013e8377042be8d2021-04-24T23:00:28ZengMDPI AGWater2073-44412021-04-01131174117410.3390/w13091174Spatiotemporal Effects and Driving Factors of Water Pollutants Discharge in Beijing–Tianjin–Hebei RegionQilong Ren0Hui Li1School of Urban and Environmental Science, Huaiyin Normal University, Huai’an 223300, ChinaCollege of Resources Environment and Tourism, Capital Normal University, Beijing 100048, ChinaThe problem of water pollution is a social issue in China requiring immediate and urgent solutions. In the Beijing–Tianjin–Hebei region, the contradiction between preserving the ecological environment and facilitating sustainable economic development is particularly acute. This study analyzed the spatiotemporal evolution of water pollutants and their factors of influence using statistics on the discharge of two water pollutants, namely chemical oxygen demand (COD) and NH<sub>3</sub>-N (ammonia nitrogen), in 154 counties in both 2012 and 2016 as research units in the region. The study employed Exploratory Spatial-Time Data Analysis (ESTDA), Standard Deviational Ellipse (SDE), and the Geographically Weighted Regression (GWR) models, as well as ArcGIS and GeoDa software, obtaining the following conclusions: (1) From 2012 to 2016, pollutant discharge dropped significantly, with COD and NH<sub>3</sub>-N emissions decreasing 65.9% and 47.2%, respectively; the pollutant emissions possessed the spatial feature of gradual gradient descent from the central districts to the periphery. (2) The water pollutants discharge displayed significant and positive spatial correlations. The spatiotemporal cohesion of the spatiotemporal evolution of the pollutants was higher than their spatiotemporal fluidity, representing strong spatial locking. (3) The level of economic development, the level of urbanization, and the intensity of agricultural production input significantly and positively drove pollutant discharge; the environmental regulations had a significant effect on reducing the emission of pollutants. In particular, the effect for NH<sub>3</sub>-N emissions reduction was stronger; the driving effect of the industrial structure and the distance decay was not significant.https://www.mdpi.com/2073-4441/13/9/1174driving factorsgeographical weighted regressionspatial heterogeneityspatiotemporal evolution |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Qilong Ren Hui Li |
spellingShingle |
Qilong Ren Hui Li Spatiotemporal Effects and Driving Factors of Water Pollutants Discharge in Beijing–Tianjin–Hebei Region Water driving factors geographical weighted regression spatial heterogeneity spatiotemporal evolution |
author_facet |
Qilong Ren Hui Li |
author_sort |
Qilong Ren |
title |
Spatiotemporal Effects and Driving Factors of Water Pollutants Discharge in Beijing–Tianjin–Hebei Region |
title_short |
Spatiotemporal Effects and Driving Factors of Water Pollutants Discharge in Beijing–Tianjin–Hebei Region |
title_full |
Spatiotemporal Effects and Driving Factors of Water Pollutants Discharge in Beijing–Tianjin–Hebei Region |
title_fullStr |
Spatiotemporal Effects and Driving Factors of Water Pollutants Discharge in Beijing–Tianjin–Hebei Region |
title_full_unstemmed |
Spatiotemporal Effects and Driving Factors of Water Pollutants Discharge in Beijing–Tianjin–Hebei Region |
title_sort |
spatiotemporal effects and driving factors of water pollutants discharge in beijing–tianjin–hebei region |
publisher |
MDPI AG |
series |
Water |
issn |
2073-4441 |
publishDate |
2021-04-01 |
description |
The problem of water pollution is a social issue in China requiring immediate and urgent solutions. In the Beijing–Tianjin–Hebei region, the contradiction between preserving the ecological environment and facilitating sustainable economic development is particularly acute. This study analyzed the spatiotemporal evolution of water pollutants and their factors of influence using statistics on the discharge of two water pollutants, namely chemical oxygen demand (COD) and NH<sub>3</sub>-N (ammonia nitrogen), in 154 counties in both 2012 and 2016 as research units in the region. The study employed Exploratory Spatial-Time Data Analysis (ESTDA), Standard Deviational Ellipse (SDE), and the Geographically Weighted Regression (GWR) models, as well as ArcGIS and GeoDa software, obtaining the following conclusions: (1) From 2012 to 2016, pollutant discharge dropped significantly, with COD and NH<sub>3</sub>-N emissions decreasing 65.9% and 47.2%, respectively; the pollutant emissions possessed the spatial feature of gradual gradient descent from the central districts to the periphery. (2) The water pollutants discharge displayed significant and positive spatial correlations. The spatiotemporal cohesion of the spatiotemporal evolution of the pollutants was higher than their spatiotemporal fluidity, representing strong spatial locking. (3) The level of economic development, the level of urbanization, and the intensity of agricultural production input significantly and positively drove pollutant discharge; the environmental regulations had a significant effect on reducing the emission of pollutants. In particular, the effect for NH<sub>3</sub>-N emissions reduction was stronger; the driving effect of the industrial structure and the distance decay was not significant. |
topic |
driving factors geographical weighted regression spatial heterogeneity spatiotemporal evolution |
url |
https://www.mdpi.com/2073-4441/13/9/1174 |
work_keys_str_mv |
AT qilongren spatiotemporaleffectsanddrivingfactorsofwaterpollutantsdischargeinbeijingtianjinhebeiregion AT huili spatiotemporaleffectsanddrivingfactorsofwaterpollutantsdischargeinbeijingtianjinhebeiregion |
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1721511033050234880 |